{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VIBg-77CTFNv",
    "colab_type": "text"
   },
   "source": [
    "# 利用thchs30为例建立一个语音识别系统\n",
    "\n",
    "\n",
    "- 数据处理\n",
    "- 搭建模型\n",
    "    - DFCNN\n",
    "\n",
    "论文地址：http://www.infocomm-journal.com/dxkx/CN/article/downloadArticleFile.do?attachType=PDF&id=166970"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "77-GlfiJTLhK",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "#!wget http://www.openslr.org/resources/18/data_thchs30.tgz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "VIG-_cv-TXMa",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "#!tar zxvf data_thchs30.tgz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "f-rnXHFdYddT",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    ""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MOtaxs9iTFNw",
    "colab_type": "text"
   },
   "source": [
    "## 1. 特征提取\n",
    "\n",
    "input为输入音频数据，需要转化为频谱图数据，然后通过cnn处理图片的能力进行识别。\n",
    "\n",
    "**1. 读取音频文件**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false,
    "id": "12mkEVxXTFNx",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 347.0
    },
    "outputId": "63d33a3a-2513-4d31-aa54-ff5ae0835112"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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v10sRHKTD/N8NRLvWzdClnfVu3JAg91ssj0Z1dPsaniZWy8xSl7SnBIiY00Ms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\nlxdgyaDGnAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f249658ecc0>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.io.wavfile as wav\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "# 随意搞个音频做实验\n",
    "filepath = 'test.wav'\n",
    "\n",
    "fs, wavsignal = wav.read(filepath)\n",
    "\n",
    "plt.plot(wavsignal)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RQNfK86uTFN2",
    "colab_type": "text"
   },
   "source": [
    "**2. 构造汉明窗**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "ZtdkDTRhTFN3",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 347.0
    },
    "outputId": "ed59b566-bc83-42ab-ced2-884712219c33"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZA6FW8deL6E70vlrMSItFU0s7dh7lKlreip+S5DIWqx17T19FiEGHGelxouMQ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\nIkH+PyN0WAPI2/oKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2495e12a90>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "x=np.linspace(0, 400 - 1, 400, dtype = np.int64)\n",
    "w = 0.54 - 0.46 * np.cos(2 * np.pi * (x) / (400 - 1))\n",
    "plt.plot(w)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9DryS5jYTFN7",
    "colab_type": "text"
   },
   "source": [
    "**3. 对数据分帧**\n",
    "\n",
    "- 帧长： 25ms\n",
    "- 帧移： 10ms\n",
    "\n",
    "\n",
    "```\n",
    "采样点（s） = fs\n",
    "采样点（ms）= fs / 1000\n",
    "采样点（帧）= fs / 1000 * 帧长\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "f3d59-6jTFN7",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "time_window = 25\n",
    "window_length = fs // 1000 * time_window"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kx8VYd5BTFN-",
    "colab_type": "text"
   },
   "source": [
    "**4. 分帧加窗**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "6o3Yn8qmTFN-",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 677.0
    },
    "outputId": "d1a41ffb-d7e6-450e-db91-997ff527d346"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZXfmEGhj7oOwVBfF+YW7v1uaY6z0hx6LSxphMZloWfvL4Tqz/+XZsfeu4a+KY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\nEARB2Bgy1ARBEARhY/4/Mn5OvCPuvOgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f24935c4eb8>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MRiMvvvgimzZt4ic/+QkAmzZtYsOGDZSVleF0Otm1axe1tbVs27aNF154gaee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\nMTZqIiIiGWOjJiIikjE2aiIiIhn7H5bTo8DYt2moAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f24934d1d68>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分帧\n",
    "p_begin = 0\n",
    "p_end = p_begin + window_length\n",
    "frame = wavsignal[p_begin:p_end]\n",
    "plt.plot(frame)\n",
    "plt.show()\n",
    "# 加窗\n",
    "frame = frame * w\n",
    "plt.plot(frame)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VC_PGtWoTFOB",
    "colab_type": "text"
   },
   "source": [
    "**5. 傅里叶变换**\n",
    "\n",
    "所谓时频图就是将时域信息转换到频域上去，具体原理可百度。人耳感知声音是通过"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "5CnLQZnHTFOC",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 677.0
    },
    "outputId": "3d47a819-10f7-4221-efe4-5d9fd950069c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LJGn27NlqbGzUnj17NHnyZIXDYQWDQU2fPl1NTU1qbGzUnDlzJEl1dXVqampSKpXSwYMH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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f249349cd68>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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72zke9bfUPY15UAqlKs9gZ5Zvsc2Y79mJZVRVM94L6Bs+1mjjhXP6Atdow+MV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2492fb85c0>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.fftpack import fft\n",
    "\n",
    "# 进行快速傅里叶变换\n",
    "frame_fft = np.abs(fft(frame))[:200]\n",
    "plt.plot(frame_fft)\n",
    "plt.show()\n",
    "\n",
    "# 取对数，求db\n",
    "frame_log = np.log(frame_fft)\n",
    "plt.plot(frame_log)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bxLC8NKeTFOF",
    "colab_type": "text"
   },
   "source": [
    "- 分帧\n",
    "- 加窗\n",
    "- 傅里叶变换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "9zrhkEr_TFOG",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.io.wavfile as wav\n",
    "from scipy.fftpack import fft\n",
    "\n",
    "\n",
    "# 获取信号的时频图\n",
    "def compute_fbank(file):\n",
    "\tx=np.linspace(0, 400 - 1, 400, dtype = np.int64)\n",
    "\tw = 0.54 - 0.46 * np.cos(2 * np.pi * (x) / (400 - 1) ) # 汉明窗\n",
    "\tfs, wavsignal = wav.read(file)\n",
    "\t# wav波形 加时间窗以及时移10ms\n",
    "\ttime_window = 25 # 单位ms\n",
    "\twindow_length = fs / 1000 * time_window # 计算窗长度的公式，目前全部为400固定值\n",
    "\twav_arr = np.array(wavsignal)\n",
    "\twav_length = len(wavsignal)\n",
    "\trange0_end = int(len(wavsignal)/fs*1000 - time_window) // 10 # 计算循环终止的位置，也就是最终生成的窗数\n",
    "\tdata_input = np.zeros((range0_end, 200), dtype = np.float) # 用于存放最终的频率特征数据\n",
    "\tdata_line = np.zeros((1, 400), dtype = np.float)\n",
    "\tfor i in range(0, range0_end):\n",
    "\t\tp_start = i * 160\n",
    "\t\tp_end = p_start + 400\n",
    "\t\tdata_line = wav_arr[p_start:p_end]\t\n",
    "\t\tdata_line = data_line * w # 加窗\n",
    "\t\tdata_line = np.abs(fft(data_line))\n",
    "\t\tdata_input[i]=data_line[0:200] # 设置为400除以2的值（即200）是取一半数据，因为是对称的\n",
    "\tdata_input = np.log(data_input + 1)\n",
    "\t#data_input = data_input[::]\n",
    "\treturn data_input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JDPZHdHiTFOI",
    "colab_type": "text"
   },
   "source": [
    "\n",
    "- 该函数提取音频文件的时频图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true,
    "id": "_YKOAoZhTFOJ",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143.0
    },
    "outputId": "6b2ef385-a873-4daa-837b-12faf2ac3dc6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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v4+rVq/Ie6/U6ms0mCoUCPB4PXC4X9vf3pZBrtVo4PT19DOJmMqMACzjrLtPp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2492f4b780>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "filepath = 'test.wav'\n",
    "\n",
    "a = compute_fbank(filepath)\n",
    "plt.imshow(a.T, origin = 'lower')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Gxj0Uv_0TFON",
    "colab_type": "text"
   },
   "source": [
    "## 2. 数据处理\n",
    "\n",
    "#### 下载数据\n",
    "thchs30: http://www.openslr.org/18/\n",
    "\n",
    "### 2.1 生成音频文件和标签文件列表\n",
    "考虑神经网络训练过程中接收的输入输出。首先需要batch_size内数据需要统一数据的shape。\n",
    "\n",
    "**格式为**：[batch_size, time_step, feature_dim]\n",
    "\n",
    "然而读取的每一个sample的时间轴长都不一样，所以需要对时间轴进行处理，选择batch内最长的那个时间为基准，进行padding。这样一个batch内的数据都相同，就能进行并行训练啦。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "bTMcXzMgTFOO",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "source_file = 'data_thchs30'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ECHpbyuMTFOQ",
    "colab_type": "text"
   },
   "source": [
    "#### 定义函数`source_get`，获取音频文件及标注文件列表\n",
    "\n",
    "形如：\n",
    "```\n",
    "E:\\Data\\thchs30\\data_thchs30\\data\\A11_0.wav.trn\n",
    "E:\\Data\\thchs30\\data_thchs30\\data\\A11_1.wav.trn\n",
    "E:\\Data\\thchs30\\data_thchs30\\data\\A11_10.wav.trn\n",
    "E:\\Data\\thchs30\\data_thchs30\\data\\A11_100.wav.trn\n",
    "E:\\Data\\thchs30\\data_thchs30\\data\\A11_102.wav.trn\n",
    "E:\\Data\\thchs30\\data_thchs30\\data\\A11_103.wav.trn\n",
    "E:\\Data\\thchs30\\data_thchs30\\data\\A11_104.wav.trn\n",
    "E:\\Data\\thchs30\\data_thchs30\\data\\A11_105.wav.trn\n",
    "E:\\Data\\thchs30\\data_thchs30\\data\\A11_106.wav.trn\n",
    "E:\\Data\\thchs30\\data_thchs30\\data\\A11_107.wav.trn\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "IC8m_vKJTFOR",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 73.0
    },
    "outputId": "48bdcbca-3a76-49ea-ed0c-676dec6c486c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['data_thchs30/data/A23_73.wav.trn', 'data_thchs30/data/C4_681.wav.trn', 'data_thchs30/data/D12_793.wav.trn', 'data_thchs30/data/A19_137.wav.trn', 'data_thchs30/data/D11_898.wav.trn', 'data_thchs30/data/B33_491.wav.trn', 'data_thchs30/data/C7_546.wav.trn', 'data_thchs30/data/C32_671.wav.trn', 'data_thchs30/data/D32_817.wav.trn', 'data_thchs30/data/A32_115.wav.trn']\n",
      "['data_thchs30/data/A23_73.wav', 'data_thchs30/data/C4_681.wav', 'data_thchs30/data/D12_793.wav', 'data_thchs30/data/A19_137.wav', 'data_thchs30/data/D11_898.wav', 'data_thchs30/data/B33_491.wav', 'data_thchs30/data/C7_546.wav', 'data_thchs30/data/C32_671.wav', 'data_thchs30/data/D32_817.wav', 'data_thchs30/data/A32_115.wav']\n"
     ]
    }
   ],
   "source": [
    "def source_get(source_file):\n",
    "    train_file = source_file + '/data'\n",
    "    label_lst = []\n",
    "    wav_lst = []\n",
    "    for root, dirs, files in os.walk(train_file):\n",
    "        for file in files:\n",
    "            if file.endswith('.wav') or file.endswith('.WAV'):\n",
    "                wav_file = os.sep.join([root, file])\n",
    "                label_file = wav_file + '.trn'\n",
    "                wav_lst.append(wav_file)\n",
    "                label_lst.append(label_file)\n",
    "            \n",
    "    return label_lst, wav_lst\n",
    "\n",
    "label_lst, wav_lst = source_get(source_file)\n",
    "\n",
    "print(label_lst[:10])\n",
    "print(wav_lst[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bMZGx3oaTFOT",
    "colab_type": "text"
   },
   "source": [
    "#### 确认相同id对应的音频文件和标签文件相同"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "rx1aplrVTFOT",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "for i in range(10000):\n",
    "    wavname = (wav_lst[i].split('/')[-1]).split('.')[0]\n",
    "    labelname = (label_lst[i].split('/')[-1]).split('.')[0]\n",
    "    if wavname != labelname:\n",
    "        print('error')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FRhD_BV8TFOV",
    "colab_type": "text"
   },
   "source": [
    "### 2.2 label数据处理\n",
    "#### 定义函数`read_label`读取音频文件对应的拼音label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "Paw8s3A8TFOW",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71.0
    },
    "outputId": "66099c8d-c4a7-4cb3-ce73-dd3ec48e1896"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zhe4 ci4 quan2 guo2 qing1 nian2 pai2 qiu2 lian2 sai4 gong4 she4 tian1 jin1 zhou1 shan1 wu3 han4 san1 ge5 sai4 qu1 mei3 ge5 sai4 qu1 de5 qian2 liang3 ming2 jiang4 can1 jia1 fu4 sai4\n",
      "\n",
      "13388\n"
     ]
    }
   ],
   "source": [
    "def read_label(label_file):\n",
    "    with open(label_file, 'r', encoding='utf8') as f:\n",
    "        data = f.readlines()\n",
    "        return data[1]\n",
    "\n",
    "print(read_label(label_lst[0]))\n",
    "\n",
    "def gen_label_data(label_lst):\n",
    "    label_data = []\n",
    "    for label_file in label_lst:\n",
    "        pny = read_label(label_file)\n",
    "        label_data.append(pny.strip('\\n'))\n",
    "    return label_data\n",
    "\n",
    "label_data = gen_label_data(label_lst)\n",
    "print(len(label_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EAC0XpSeTFOZ",
    "colab_type": "text"
   },
   "source": [
    "#### 为label建立拼音到id的映射，即词典"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "b_tPrz9sTFOa",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35.0
    },
    "outputId": "67c85692-c6e3-42d5-d51d-7f58e1643b85"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1209\n"
     ]
    }
   ],
   "source": [
    "def mk_vocab(label_data):\n",
    "    vocab = []\n",
    "    for line in label_data:\n",
    "        line = line.split(' ')\n",
    "        for pny in line:\n",
    "            if pny not in vocab:\n",
    "                vocab.append(pny)\n",
    "    vocab.append('_')\n",
    "    return vocab\n",
    "\n",
    "vocab = mk_vocab(label_data)\n",
    "print(len(vocab))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1Rr9kgrpTFOg",
    "colab_type": "text"
   },
   "source": [
    "#### 有了词典就能将读取到的label映射到对应的id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "LfgQDpjbTFOg",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 53.0
    },
    "outputId": "6a561916-7960-4c2e-ef46-241a8aa9a872"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zhe4 ci4 quan2 guo2 qing1 nian2 pai2 qiu2 lian2 sai4 gong4 she4 tian1 jin1 zhou1 shan1 wu3 han4 san1 ge5 sai4 qu1 mei3 ge5 sai4 qu1 de5 qian2 liang3 ming2 jiang4 can1 jia1 fu4 sai4\n",
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 9, 20, 21, 19, 9, 20, 22, 23, 24, 25, 26, 27, 28, 29, 9]\n"
     ]
    }
   ],
   "source": [
    "def word2id(line, vocab):\n",
    "    return [vocab.index(pny) for pny in line.split(' ')]\n",
    "\n",
    "label_id = word2id(label_data[0], vocab)\n",
    "print(label_data[0])\n",
    "print(label_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5jLX4T9hTFOo",
    "colab_type": "text"
   },
   "source": [
    "#### 总结:\n",
    "我们提取出了每个音频文件对应的拼音标签`label_data`，通过索引就可以获得该索引的标签。\n",
    "\n",
    "也生成了对应的拼音词典.由此词典，我们可以映射拼音标签为id序列。\n",
    "\n",
    "输出：\n",
    "- vocab\n",
    "- label_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "uH4Wq9BdTFOo",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71.0
    },
    "outputId": "d400a47b-a2ba-4977-edb8-c9599854273b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['zhe4', 'ci4', 'quan2', 'guo2', 'qing1', 'nian2', 'pai2', 'qiu2', 'lian2', 'sai4', 'gong4', 'she4', 'tian1', 'jin1', 'zhou1']\n",
      "can1 jin1 shi4 ca1 zui3 he2 shou2 zhi3 de5 bei1 bian1 qing3 yong4 can1 zhi3 ca1 shi4 lian3 shang4 de5 han4 huo4 zhan1 shang5 de5 shui3 zhi1 qing3 yong4 zi4 ji3 de5 shou3 juan4 ca1 diao4\n",
      "[27, 13, 199, 200, 201, 63, 202, 203, 22, 204, 205, 206, 207, 27, 203, 200, 199, 208, 120, 22, 17, 209, 210, 211, 22, 31, 212, 206, 207, 213, 214, 22, 215, 216, 200, 217]\n"
     ]
    }
   ],
   "source": [
    "print(vocab[:15])\n",
    "print(label_data[10])\n",
    "print(word2id(label_data[10], vocab))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7nkZQNioTFOr",
    "colab_type": "text"
   },
   "source": [
    "### 2.3 音频数据处理\n",
    "\n",
    "音频数据处理，只需要获得对应的音频文件名，然后提取所需时频图即可。\n",
    "\n",
    "其中`compute_fbank`时频转化的函数在前面已经定义好了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "g5s_Skl3TFOr",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35.0
    },
    "outputId": "786bd9da-e690-494e-cc12-33b8813c9c2d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1026, 200)\n"
     ]
    }
   ],
   "source": [
    "fbank = compute_fbank(wav_lst[0])\n",
    "print(fbank.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "tymo7MeETFOu",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 139.0
    },
    "outputId": "3ad3cbe9-f721-4e54-faef-1475c532e7f9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GG2/Y9xN08VygE6urq7p586ahCmSCzBtziDiKAwc9AYcYDodPKHPhnBAhkvlA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\nIBdkiUQikUjWAP8DR4u4j97U2mIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2493549da0>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(fbank.T, origin = 'lower')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_vQWPblSTFOw",
    "colab_type": "text"
   },
   "source": [
    "#### 由于声学模型网络结构原因（3个maxpooling层），我们的音频数据的每个维度需要能够被8整除。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "O3D9NlljTFOx",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "fbank = fbank[:fbank.shape[0]//8*8, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "Md4gYnFrTFOy",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35.0
    },
    "outputId": "ac0dfa1f-39f7-47fc-b397-a24a9e6b2207"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1024, 200)\n"
     ]
    }
   ],
   "source": [
    "print(fbank.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "w0LQ11PkTFO1",
    "colab_type": "text"
   },
   "source": [
    "#### 总结：\n",
    "- 对音频数据进行时频转换\n",
    "- 转换后的数据需要各个维度能够被8整除\n",
    "\n",
    "### 2.4 数据生成器\n",
    "#### 确定batch_size和batch_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "tUmMcmhATFO1",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "total_nums = 10000\n",
    "batch_size = 4\n",
    "batch_num = total_nums // batch_size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qUxHfxe4TFO4",
    "colab_type": "text"
   },
   "source": [
    "#### shuffle\n",
    "打乱数据的顺序，我们通过查询乱序的索引值，来确定训练数据的顺序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "ejqsLFJdTFO4",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "from random import shuffle\n",
    "shuffle_list = [i for i in range(10000)]\n",
    "shuffle(shuffle_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YD4qWOq0TFO6",
    "colab_type": "text"
   },
   "source": [
    "#### generator\n",
    "batch_size的信号时频图和标签数据，存放到两个list中去"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "ccsv7q1rTFO7",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "def get_batch(batch_size, shuffle_list, wav_lst, label_data, vocab):\n",
    "    for i in range(10000//batch_size):\n",
    "        wav_data_lst = []\n",
    "        label_data_lst = []\n",
    "        begin = i * batch_size\n",
    "        end = begin + batch_size\n",
    "        sub_list = shuffle_list[begin:end]\n",
    "        for index in sub_list:\n",
    "            fbank = compute_fbank(wav_lst[index])\n",
    "            fbank = fbank[:fbank.shape[0] // 8 * 8, :]\n",
    "            label = word2id(label_data[index], vocab)\n",
    "            wav_data_lst.append(fbank)\n",
    "            label_data_lst.append(label)\n",
    "        yield wav_data_lst, label_data_lst\n",
    "\n",
    "batch = get_batch(4, shuffle_list, wav_lst, label_data, vocab)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "gNfYVbEhTFO8",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 161.0
    },
    "outputId": "f640a09c-0b19-437e-e3b7-a0a6c02d116e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1272, 200)\n",
      "(792, 200)\n",
      "(872, 200)\n",
      "(928, 200)\n",
      "[27, 13, 199, 200, 201, 63, 202, 203, 22, 204, 205, 206, 207, 27, 203, 200, 199, 208, 120, 22, 17, 209, 210, 211, 22, 31, 212, 206, 207, 213, 214, 22, 215, 216, 200, 217]\n",
      "[731, 5, 353, 301, 344, 41, 36, 212, 250, 103, 246, 199, 22, 766, 16, 380, 243, 411, 420, 9, 206, 259, 435, 244, 249, 113, 245, 344, 41, 188, 70]\n",
      "[0, 674, 444, 316, 20, 22, 103, 117, 199, 392, 376, 512, 519, 118, 438, 22, 328, 308, 58, 63, 1065, 198, 624, 472, 232, 159, 163, 199, 392, 376, 512, 519, 173, 22]\n",
      "[39, 51, 393, 471, 537, 198, 58, 535, 632, 100, 655, 63, 226, 488, 69, 376, 190, 409, 8, 349, 242, 93, 305, 1012, 369, 172, 166, 58, 156, 305, 179, 274, 44, 435]\n"
     ]
    }
   ],
   "source": [
    "wav_data_lst, label_data_lst = next(batch)\n",
    "for wav_data in wav_data_lst:\n",
    "    print(wav_data.shape)\n",
    "for label_data in label_data_lst:\n",
    "    print(label_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "EtpmJxsaTFO-",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 53.0
    },
    "outputId": "2439a771-8413-4746-f4ed-d3824b5bc7d4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1272\n",
      "[1272, 792, 872, 928]\n"
     ]
    }
   ],
   "source": [
    "lens = [len(wav) for wav in wav_data_lst]\n",
    "print(max(lens))\n",
    "print(lens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QRcBRBo_TFPA",
    "colab_type": "text"
   },
   "source": [
    "#### padding\n",
    "然而，每一个batch_size内的数据有一个要求，就是需要构成成一个tensorflow块，这就要求每个样本数据形式是一样的。\n",
    "除此之外，ctc需要获得的信息还有输入序列的长度。\n",
    "这里输入序列经过卷积网络后，长度缩短了8倍，因此我们训练实际输入的数据为wav_len//8。\n",
    "- padding wav data\n",
    "- wav len // 8 （网络结构导致的）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "4lKAfKnKTFPB",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 53.0
    },
    "outputId": "718126fd-198b-4d9e-efd4-74acaf0190a5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4, 1272, 200, 1)\n",
      "[159  99 109 116]\n"
     ]
    }
   ],
   "source": [
    "def wav_padding(wav_data_lst):\n",
    "    wav_lens = [len(data) for data in wav_data_lst]\n",
    "    wav_max_len = max(wav_lens)\n",
    "    wav_lens = np.array([leng//8 for leng in wav_lens])\n",
    "    new_wav_data_lst = np.zeros((len(wav_data_lst), wav_max_len, 200, 1))\n",
    "    for i in range(len(wav_data_lst)):\n",
    "        new_wav_data_lst[i, :wav_data_lst[i].shape[0], :, 0] = wav_data_lst[i]\n",
    "    return new_wav_data_lst, wav_lens\n",
    "\n",
    "pad_wav_data_lst, wav_lens = wav_padding(wav_data_lst)\n",
    "print(pad_wav_data_lst.shape)\n",
    "print(wav_lens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ttw65iZvTFPD",
    "colab_type": "text"
   },
   "source": [
    "同样也要对label进行padding和长度获取，不同的是数据维度不同，且label的长度就是输入给ctc的长度，不需要额外处理\n",
    "- label padding\n",
    "- label len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "sW1HcU5PTFPD",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 53.0
    },
    "outputId": "c9f8219e-f71c-4620-cf3e-33144d6b3d8b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4, 36)\n",
      "[36 31 34 34]\n"
     ]
    }
   ],
   "source": [
    "def label_padding(label_data_lst):\n",
    "    label_lens = np.array([len(label) for label in label_data_lst])\n",
    "    max_label_len = max(label_lens)\n",
    "    new_label_data_lst = np.zeros((len(label_data_lst), max_label_len))\n",
    "    for i in range(len(label_data_lst)):\n",
    "        new_label_data_lst[i][:len(label_data_lst[i])] = label_data_lst[i]\n",
    "    return new_label_data_lst, label_lens\n",
    "\n",
    "pad_label_data_lst, label_lens = label_padding(label_data_lst)\n",
    "print(pad_label_data_lst.shape)\n",
    "print(label_lens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QzhiqU4sTFPG",
    "colab_type": "text"
   },
   "source": [
    "- 用于训练格式的数据生成器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "CjLXnThATFPG",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "def data_generator(batch_size, shuffle_list, wav_lst, label_data, vocab):\n",
    "    for i in range(len(wav_lst)//batch_size):\n",
    "        wav_data_lst = []\n",
    "        label_data_lst = []\n",
    "        begin = i * batch_size\n",
    "        end = begin + batch_size\n",
    "        sub_list = shuffle_list[begin:end]\n",
    "        for index in sub_list:\n",
    "            fbank = compute_fbank(wav_lst[index])\n",
    "            pad_fbank = np.zeros((fbank.shape[0]//8*8+8, fbank.shape[1]))\n",
    "            pad_fbank[:fbank.shape[0], :] = fbank\n",
    "            label = word2id(label_data[index], vocab)\n",
    "            wav_data_lst.append(pad_fbank)\n",
    "            label_data_lst.append(label)\n",
    "        pad_wav_data, input_length = wav_padding(wav_data_lst)\n",
    "        pad_label_data, label_length = label_padding(label_data_lst)\n",
    "        inputs = {'the_inputs': pad_wav_data,\n",
    "                  'the_labels': pad_label_data,\n",
    "                  'input_length': input_length,\n",
    "                  'label_length': label_length,\n",
    "                 }\n",
    "        outputs = {'ctc': np.zeros(pad_wav_data.shape[0],)} \n",
    "        yield inputs, outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "-A0wB8VoTFPI",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    ""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5MKnT0MRTFPJ",
    "colab_type": "text"
   },
   "source": [
    "## 3. 模型搭建\n",
    "\n",
    "训练输入为时频图，标签为对应的拼音标签，如下所示：\n",
    "\n",
    "\n",
    "搭建语音识别模型，采用了 CNN+CTC 的结构。\n",
    "![dfcnn.jpg](attachment:dfcnn.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "YzxubHEHTFPK",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35.0
    },
    "outputId": "27ad0557-19e7-4514-d506-bfc933b656ee"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.layers import Input, Conv2D, BatchNormalization, MaxPooling2D\n",
    "from keras.layers import Reshape, Dense, Lambda\n",
    "from keras.optimizers import Adam\n",
    "from keras import backend as K\n",
    "from keras.models import Model\n",
    "from keras.utils import multi_gpu_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Cn02BtPCTFPL",
    "colab_type": "text"
   },
   "source": [
    "- 定义3*3的卷积层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "kQsJljWTTFPN",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "def conv2d(size):\n",
    "    return Conv2D(size, (3,3), use_bias=True, activation='relu',\n",
    "        padding='same', kernel_initializer='he_normal')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vy8NBYcSTFPR",
    "colab_type": "text"
   },
   "source": [
    "- 定义batch norm层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "N3LroAuTTFPR",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "def norm(x):\n",
    "    return BatchNormalization(axis=-1)(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hhOGw1CETFPU",
    "colab_type": "text"
   },
   "source": [
    "- 定义最大池化层，数据的后两维维度都减半"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "-ZU5qMysTFPV",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "def maxpool(x):\n",
    "    return MaxPooling2D(pool_size=(2,2), strides=None, padding=\"valid\")(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Osh8shMYTFPY",
    "colab_type": "text"
   },
   "source": [
    "- dense层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "cZrqYemFTFPY",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "def dense(units, activation=\"relu\"):\n",
    "    return Dense(units, activation=activation, use_bias=True,\n",
    "        kernel_initializer='he_normal')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qIIIQ8tiTFPa",
    "colab_type": "text"
   },
   "source": [
    "- 由cnn + cnn + maxpool构成的组合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "3m_eAg-ETFPb",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "# x.shape=(none, none, none)\n",
    "# output.shape = (1/2, 1/2, 1/2)\n",
    "def cnn_cell(size, x, pool=True):\n",
    "    x = norm(conv2d(size)(x))\n",
    "    x = norm(conv2d(size)(x))\n",
    "    if pool:\n",
    "        x = maxpool(x)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jhtoya1WTFPd",
    "colab_type": "text"
   },
   "source": [
    "- **添加CTC损失函数，由backend引入**\n",
    "\n",
    "**注意：CTC_batch_cost输入为：**\n",
    "\n",
    "- **labels** 标签：[batch_size, l]\n",
    "- **y_pred** cnn网络的输出：[batch_size, t, vocab_size]\n",
    "- **input_length** 网络输出的长度：[batch_size]\n",
    "- **label_length** 标签的长度：[batch_size]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "YX8s5naeTFPd",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "def ctc_lambda(args):\n",
    "    labels, y_pred, input_length, label_length = args\n",
    "    y_pred = y_pred[:, :, :]\n",
    "    return K.ctc_batch_cost(labels, y_pred, input_length, label_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rDl6EDj2TFPf",
    "colab_type": "text"
   },
   "source": [
    "### **搭建cnn+dnn+ctc的声学模型**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "DBF5GrQsTFPg",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "class Amodel():\n",
    "    \"\"\"docstring for Amodel.\"\"\"\n",
    "    def __init__(self, vocab_size):\n",
    "        super(Amodel, self).__init__()\n",
    "        self.vocab_size = vocab_size\n",
    "        self._model_init()\n",
    "        self._ctc_init()\n",
    "        self.opt_init()\n",
    "\n",
    "    def _model_init(self):\n",
    "        self.inputs = Input(name='the_inputs', shape=(None, 200, 1))\n",
    "        self.h1 = cnn_cell(32, self.inputs)\n",
    "        self.h2 = cnn_cell(64, self.h1)\n",
    "        self.h3 = cnn_cell(128, self.h2)\n",
    "        self.h4 = cnn_cell(128, self.h3, pool=False)\n",
    "        # 200 / 8 * 128 = 3200\n",
    "        self.h6 = Reshape((-1, 3200))(self.h4)\n",
    "        self.h7 = dense(256)(self.h6)\n",
    "        self.outputs = dense(self.vocab_size, activation='softmax')(self.h7)\n",
    "        self.model = Model(inputs=self.inputs, outputs=self.outputs)\n",
    "\n",
    "    def _ctc_init(self):\n",
    "        self.labels = Input(name='the_labels', shape=[None], dtype='float32')\n",
    "        self.input_length = Input(name='input_length', shape=[1], dtype='int64')\n",
    "        self.label_length = Input(name='label_length', shape=[1], dtype='int64')\n",
    "        self.loss_out = Lambda(ctc_lambda, output_shape=(1,), name='ctc')\\\n",
    "            ([self.labels, self.outputs, self.input_length, self.label_length])\n",
    "        self.ctc_model = Model(inputs=[self.labels, self.inputs,\n",
    "            self.input_length, self.label_length], outputs=self.loss_out)\n",
    "\n",
    "    def opt_init(self):\n",
    "        opt = Adam(lr = 0.0008, beta_1 = 0.9, beta_2 = 0.999, decay = 0.01, epsilon = 10e-8)\n",
    "        #self.ctc_model=multi_gpu_model(self.ctc_model,gpus=2)\n",
    "        self.ctc_model.compile(loss={'ctc': lambda y_true, output: output}, optimizer=opt)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "KEm3xnI_TFPh",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1169.0
    },
    "outputId": "e15f6e0c-6a2a-4e01-b50e-2734095010cb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "the_inputs (InputLayer)         (None, None, 200, 1) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, None, 200, 32 320         the_inputs[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, None, 200, 32 128         conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, None, 200, 32 9248        batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, None, 200, 32 128         conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, None, 100, 32 0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, None, 100, 64 18496       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, None, 100, 64 256         conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, None, 100, 64 36928       batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, None, 100, 64 256         conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, None, 50, 64) 0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, None, 50, 128 73856       max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, None, 50, 128 512         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, None, 50, 128 147584      batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, None, 50, 128 512         conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, None, 25, 128 0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, None, 25, 128 147584      max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, None, 25, 128 512         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, None, 25, 128 147584      batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, None, 25, 128 512         conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "reshape_1 (Reshape)             (None, None, 3200)   0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, None, 256)    819456      reshape_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "the_labels (InputLayer)         (None, None)         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, None, 1176)   302232      dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "input_length (InputLayer)       (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "label_length (InputLayer)       (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "ctc (Lambda)                    (None, 1)            0           the_labels[0][0]                 \n",
      "                                                                 dense_2[0][0]                    \n",
      "                                                                 input_length[0][0]               \n",
      "                                                                 label_length[0][0]               \n",
      "==================================================================================================\n",
      "Total params: 1,706,104\n",
      "Trainable params: 1,704,696\n",
      "Non-trainable params: 1,408\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "am = Amodel(1176)\n",
    "am.ctc_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1RfnYMzxTFPk",
    "colab_type": "text"
   },
   "source": [
    "## 4. 开始训练\n",
    "\n",
    "这样训练所需的数据，就准备完毕了，接下来可以进行训练了。我们采用如下参数训练：\n",
    "- batch_size = 4\n",
    "- batch_num = 10000 // 4\n",
    "- epochs = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LabqR5zwTFPn",
    "colab_type": "text"
   },
   "source": [
    "- **准备训练数据，shuffle是为了打乱训练数据顺序**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "s7zbpGYxTFPk",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "total_nums = 100\n",
    "batch_size = 20\n",
    "batch_num = total_nums // batch_size\n",
    "epochs = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "Owm5b_LNTFPn",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35.0
    },
    "outputId": "da2dea9f-8294-4e92-8d6b-b7e5bc995921"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "716\n"
     ]
    }
   ],
   "source": [
    "source_file = 'data_thchs30'\n",
    "label_lst, wav_lst = source_get(source_file)\n",
    "label_data = gen_label_data(label_lst[:100])\n",
    "vocab = mk_vocab(label_data)\n",
    "vocab_size = len(vocab)\n",
    "\n",
    "print(vocab_size)\n",
    "\n",
    "shuffle_list = [i for i in range(100)]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Rg60YIxITFPp",
    "colab_type": "text"
   },
   "source": [
    "- 使用fit_generator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ps_FNHSkTFPp",
    "colab_type": "text"
   },
   "source": [
    "- 开始训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "Ont2NPn1TFPq",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 2717.0
    },
    "outputId": "423532ad-9029-460d-9821-b9e96001f22d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is the 1 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 30s 6s/step - loss: 422.8893\n",
      "this is the 2 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 265.2371\n",
      "this is the 3 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 227.9766\n",
      "this is the 4 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 215.3482\n",
      "this is the 5 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 208.7948\n",
      "this is the 6 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 203.8971\n",
      "this is the 7 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 198.7856\n",
      "this is the 8 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 192.8669\n",
      "this is the 9 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 186.2708\n",
      "this is the 10 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 178.2350\n",
      "this is the 11 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 168.6683\n",
      "this is the 12 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 157.0543\n",
      "this is the 13 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 143.6083\n",
      "this is the 14 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 128.1789\n",
      "this is the 15 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 112.3659\n",
      "this is the 16 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 94.5758\n",
      "this is the 17 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 77.4529\n",
      "this is the 18 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 60.6812\n",
      "this is the 19 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 48.5192\n",
      "this is the 20 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 37.6046\n",
      "this is the 21 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 28.5541\n",
      "this is the 22 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 22.5874\n",
      "this is the 23 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 12.9289\n",
      "this is the 24 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 7.9077\n",
      "this is the 25 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 5.0112\n",
      "this is the 26 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 3.2873\n",
      "this is the 27 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 2.4182\n",
      "this is the 28 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 1.8471\n",
      "this is the 29 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 1.5014\n",
      "this is the 30 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 1.2821\n",
      "this is the 31 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 1.1341\n",
      "this is the 32 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 1.0227\n",
      "this is the 33 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 0.9367\n",
      "this is the 34 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 0.8694\n",
      "this is the 35 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 0.8143\n",
      "this is the 36 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 0.7689\n",
      "this is the 37 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 0.7291\n",
      "this is the 38 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 0.6944\n",
      "this is the 39 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 0.6638\n",
      "this is the 40 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 0.6363\n",
      "this is the 41 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 0.6114\n",
      "this is the 42 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 0.5887\n",
      "this is the 43 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 0.5678\n",
      "this is the 44 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 0.5486\n",
      "this is the 45 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 0.5308\n",
      "this is the 46 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 0.5142\n",
      "this is the 47 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 0.4988\n",
      "this is the 48 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 0.4843\n",
      "this is the 49 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 0.4708\n",
      "this is the 50 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 7s 1s/step - loss: 0.4580\n"
     ]
    }
   ],
   "source": [
    "am = Amodel(vocab_size)\n",
    "\n",
    "for k in range(epochs):\n",
    "    print('this is the', k+1, 'th epochs trainning !!!')\n",
    "    #shuffle(shuffle_list)\n",
    "    batch = data_generator(batch_size, shuffle_list, wav_lst, label_data, vocab)\n",
    "    am.ctc_model.fit_generator(batch, steps_per_epoch=batch_num, epochs=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "FtMApqK1cZ5J",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "def decode_ctc(num_result, num2word):\n",
    "\tresult = num_result[:, :, :]\n",
    "\tin_len = np.zeros((1), dtype = np.int32)\n",
    "\tin_len[0] = result.shape[1];\n",
    "\tr = K.ctc_decode(result, in_len, greedy = True, beam_width=10, top_paths=1)\n",
    "\tr1 = K.get_value(r[0][0])\n",
    "\tr1 = r1[0]\n",
    "\ttext = []\n",
    "\tfor i in r1:\n",
    "\t\ttext.append(num2word[i])\n",
    "\treturn r1, text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "tYpoGyrbqPA0",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 793.0
    },
    "outputId": "1d758a6c-b9ee-4df6-a0f6-d1b194c0162b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4303: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\n",
      "数字结果：  [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19  9 20 21 19\n",
      "  9 20 22 23 24 25 26 27 28 29  9]\n",
      "文本结果： ['zhe4', 'ci4', 'quan2', 'guo2', 'qing1', 'nian2', 'pai2', 'qiu2', 'lian2', 'sai4', 'gong4', 'she4', 'tian1', 'jin1', 'zhou1', 'shan1', 'wu3', 'han4', 'san1', 'ge5', 'sai4', 'qu1', 'mei3', 'ge5', 'sai4', 'qu1', 'de5', 'qian2', 'liang3', 'ming2', 'jiang4', 'can1', 'jia1', 'fu4', 'sai4']\n",
      "原文结果： ['zhe4', 'ci4', 'quan2', 'guo2', 'qing1', 'nian2', 'pai2', 'qiu2', 'lian2', 'sai4', 'gong4', 'she4', 'tian1', 'jin1', 'zhou1', 'shan1', 'wu3', 'han4', 'san1', 'ge5', 'sai4', 'qu1', 'mei3', 'ge5', 'sai4', 'qu1', 'de5', 'qian2', 'liang3', 'ming2', 'jiang4', 'can1', 'jia1', 'fu4', 'sai4']\n",
      "数字结果：  [30 31 32 33 34 35 36 32 37 38 39 40 41 22 15 42 43 44 41 45 46 47 48  3\n",
      " 39 49 50 51 52 42 53 54]\n",
      "文本结果： ['xian1', 'shui3', 'yan2', 'zhi4', 'fei1', 'ma2', 'zu3', 'yan2', 'chang2', 'da2', 'shi2', 'hua2', 'li3', 'de5', 'shan1', 'ya2', 'dong4', 'xue2', 'li3', 'you3', 'chun1', 'qiu1', 'zhan4', 'guo2', 'shi2', 'qi1', 'gu3', 'yue4', 'zu2', 'ya2', 'mu4', 'qun2']\n",
      "原文结果： ['xian1', 'shui3', 'yan2', 'zhi4', 'fei1', 'ma2', 'zu3', 'yan2', 'chang2', 'da2', 'shi2', 'hua2', 'li3', 'de5', 'shan1', 'ya2', 'dong4', 'xue2', 'li3', 'you3', 'chun1', 'qiu1', 'zhan4', 'guo2', 'shi2', 'qi1', 'gu3', 'yue4', 'zu2', 'ya2', 'mu4', 'qun2']\n",
      "数字结果：  [55 56 57 11 58 18 59 60 22 61 62 63 64 62 65 66 57 11 58 67 18 59 60 68\n",
      " 32 69 70 22  0 71 72 32 73 74 22 76]\n",
      "文本结果： ['wo3', 'men5', 'pai1', 'she4', 'le5', 'san1', 'wang4', 'chong1', 'de5', 'yuan3', 'jing3', 'he2', 'jin4', 'jing3', 'te4', 'bie2', 'pai1', 'she4', 'le5', 'cong2', 'san1', 'wang4', 'chong1', 'shen1', 'yan2', 'er2', 'chu1', 'de5', 'zhe4', 'tiao2', 'wan1', 'yan2', 'ni2', 'ning4', 'de5', 'lu4']\n",
      "原文结果： ['wo3', 'men5', 'pai1', 'she4', 'le5', 'san1', 'wang4', 'chong1', 'de5', 'yuan3', 'jing3', 'he2', 'jin4', 'jing3', 'te4', 'bie2', 'pai1', 'she4', 'le5', 'cong2', 'san1', 'wang4', 'chong1', 'shen1', 'yan2', 'er2', 'chu1', 'de5', 'zhe4', 'tiao2', 'wan1', 'yan2', 'ni2', 'ning4', 'de5', 'xiao3', 'lu4']\n",
      "数字结果：  [77 78 79 80 81 82 83 82 84 79 83 79 84 82 82 80 85 79 79 82 10 86]\n",
      "文本结果： ['bu2', 'qi4', 'ya3', 'bu4', 'bi4', 'su2', 'hua4', 'su2', 'wei2', 'ya3', 'hua4', 'ya3', 'wei2', 'su2', 'su2', 'bu4', 'shang1', 'ya3', 'ya3', 'su2', 'gong4', 'shang3']\n",
      "原文结果： ['bu2', 'qi4', 'ya3', 'bu4', 'bi4', 'su2', 'hua4', 'su2', 'wei2', 'ya3', 'hua4', 'ya3', 'wei2', 'su2', 'su2', 'bu4', 'shang1', 'ya3', 'ya3', 'su2', 'gong4', 'shang3']\n",
      "数字结果：  [ 87  13  88  25  89  90  91  92  93  90  94  95  96  97  98  99  84 100\n",
      "  89 101 102  96 103 104 104  77 105 106 107 108 109  28 110 111]\n",
      "文本结果： ['ru2', 'jin1', 'ta1', 'ming2', 'chuan2', 'si4', 'fang1', 'sheng1', 'bo1', 'si4', 'hai3', 'yang3', 'xie1', 'ji4', 'shu4', 'guang3', 'wei2', 'liu2', 'chuan2', 'you1', 'liang2', 'xie1', 'zhong3', 'yuan2', 'yuan2', 'bu2', 'duan4', 'shu1', 'song4', 'dao4', 'qian1', 'jia1', 'wan4', 'hu4']\n",
      "原文结果： ['ru2', 'jin1', 'ta1', 'ming2', 'chuan2', 'si4', 'fang1', 'sheng1', 'bo1', 'si4', 'hai3', 'yang3', 'xie1', 'ji4', 'shu4', 'guang3', 'wei2', 'liu2', 'chuan2', 'you1', 'liang2', 'xie1', 'zhong3', 'yuan2', 'yuan2', 'bu2', 'duan4', 'shu1', 'song4', 'dao4', 'qian1', 'jia1', 'wan4', 'hu4']\n",
      "数字结果：  [112 113 114  28  22 115 116 117 118 119 108  20 120 121 122 123  58 124\n",
      " 125 126 127 128 129 130 130 131 132 133  88 134  11]\n",
      "文本结果： ['yang2', 'dui4', 'zhang3', 'jia1', 'de5', 'er4', 'wa2', 'zi5', 'fa1', 'shao1', 'dao4', 'qu1', 'shang4', 'zhen2', 'suo3', 'kan4', 'le5', 'bing4', 'dai4', 'hui2', 'yi1', 'bao1', 'zhen1', 'yao4', 'yao4', 'yi4', 'qiong2', 'gei3', 'ta1', 'zhu4', 'she4']\n",
      "原文结果： ['yang2', 'dui4', 'zhang3', 'jia1', 'de5', 'er4', 'wa2', 'zi5', 'fa1', 'shao1', 'dao4', 'qu1', 'shang4', 'zhen2', 'suo3', 'kan4', 'le5', 'bing4', 'dai4', 'hui2', 'yi1', 'bao1', 'zhen1', 'yao4', 'yao4', 'yi4', 'qiong2', 'gei3', 'ta1', 'zhu4', 'she4']\n",
      "数字结果：  [135  89 136 112 137 138 139 112  14 123 132 140 141 142 143 144  39 145\n",
      " 146 143 144 147 148 149  37 119 150 151 152 118 153 154]\n",
      "文本结果： ['xiang1', 'chuan2', 'sui2', 'yang2', 'di4', 'nan2', 'xia4', 'yang2', 'zhou1', 'kan4', 'qiong2', 'hua1', 'tu2', 'jing1', 'huai2', 'yin1', 'shi2', 'wen2', 'de2', 'huai2', 'yin1', 'pao2', 'chu2', 'shan4', 'chang2', 'shao1', 'yu2', 'nai3', 'tu1', 'fa1', 'qi2', 'xiang3']\n",
      "原文结果： ['xiang1', 'chuan2', 'sui2', 'yang2', 'di4', 'nan2', 'xia4', 'yang2', 'zhou1', 'kan4', 'qiong2', 'hua1', 'tu2', 'jing1', 'huai2', 'yin1', 'shi2', 'wen2', 'de2', 'huai2', 'yin1', 'pao2', 'chu2', 'shan4', 'chang2', 'shao1', 'yu2', 'nai3', 'tu1', 'fa1', 'qi2', 'xiang3']\n",
      "数字结果：  [ 87  63 155 156 157  69 158 159  22  33 160 107 161 162 163 155  70  58\n",
      " 164 165  51  22 166 167 168 169 170 171 172 173 171 106 174]\n",
      "文本结果： ['ru2', 'he2', 'ti2', 'gao1', 'shao4', 'er2', 'du2', 'wu4', 'de5', 'zhi4', 'liang4', 'song4', 'qing4', 'ling2', 'ye3', 'ti2', 'chu1', 'le5', 'hen3', 'zhuo2', 'yue4', 'de5', 'jian4', 'jie3', 'na4', 'jiu4', 'shi5', 'zhua1', 'chuang4', 'zuo4', 'zhua1', 'shu1', 'gao3']\n",
      "原文结果： ['ru2', 'he2', 'ti2', 'gao1', 'shao4', 'er2', 'du2', 'wu4', 'de5', 'zhi4', 'liang4', 'song4', 'qing4', 'ling2', 'ye3', 'ti2', 'chu1', 'le5', 'hen3', 'zhuo2', 'yue4', 'de5', 'jian4', 'jie3', 'na4', 'jiu4', 'shi5', 'zhua1', 'chuang4', 'zuo4', 'zhua1', 'shu1', 'gao3']\n",
      "数字结果：  [ 88 153 120 175 138 176  10 177 178 179 137  22 180 160   3 181  18 182\n",
      " 183 184 185 127 185 118 186  22 187 188 189  43 179  78]\n",
      "文本结果： ['ta1', 'qi2', 'shang4', 'yun2', 'nan2', 'cheng2', 'gong4', 'xun4', 'lian4', 'ji1', 'di4', 'de5', 'yi2', 'liang4', 'guo2', 'chan3', 'san1', 'lun2', 'mo2', 'tuo2', 'rou2', 'yi1', 'rou2', 'fa1', 'hong2', 'de5', 'shuang1', 'yan3', 'qi3', 'dong4', 'ji1', 'qi4']\n",
      "原文结果： ['ta1', 'qi2', 'shang4', 'yun2', 'nan2', 'cheng2', 'gong4', 'xun4', 'lian4', 'ji1', 'di4', 'de5', 'yi2', 'liang4', 'guo2', 'chan3', 'san1', 'lun2', 'mo2', 'tuo2', 'rou2', 'yi1', 'rou2', 'fa1', 'hong2', 'de5', 'shuang1', 'yan3', 'qi3', 'dong4', 'ji1', 'qi4']\n",
      "数字结果：  [190  75   5 173  83  22  65 191 192 193 194 153  83 195  20  66 150 145\n",
      " 196  83  63   5  83 197 198  79  82  10  86]\n",
      "文本结果： ['ma3', 'xiao3', 'nian2', 'zuo4', 'hua4', 'de5', 'te4', 'zheng1', 'yu3', 'shen2', 'yun4', 'qi2', 'hua4', 'feng1', 'qu1', 'bie2', 'yu2', 'wen2', 'ren2', 'hua4', 'he2', 'nian2', 'hua4', 'ke3', 'wei4', 'ya3', 'su2', 'gong4', 'shang3']\n",
      "原文结果： ['ma3', 'xiao3', 'nian2', 'zuo4', 'hua4', 'de5', 'te4', 'zheng1', 'yu3', 'shen2', 'yun4', 'qi2', 'hua4', 'feng1', 'qu1', 'bie2', 'yu2', 'wen2', 'ren2', 'hua4', 'he2', 'nian2', 'hua4', 'ke3', 'wei4', 'ya3', 'su2', 'gong4', 'shang3']\n"
     ]
    }
   ],
   "source": [
    "# 测试模型 predict(x, batch_size=None, verbose=0, steps=None)\n",
    "batch = data_generator(1, shuffle_list, wav_lst, label_data, vocab)\n",
    "for i in range(10):\n",
    "  # 载入训练好的模型，并进行识别\n",
    "  inputs, outputs = next(batch)\n",
    "  x = inputs['the_inputs']\n",
    "  y = inputs['the_labels'][0]\n",
    "  result = am.model.predict(x, steps=1)\n",
    "  # 将数字结果转化为文本结果\n",
    "  result, text = decode_ctc(result, vocab)\n",
    "  print('数字结果： ', result)\n",
    "  print('文本结果：', text)\n",
    "  print('原文结果：', [vocab[int(i)] for i in y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "PdhZsASsrRFW",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    ""
   ]
  }
 ],
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